Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells5166
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 3 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 2 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 2 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 2 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 2 other fieldsHigh correlation
SO2 is highly overall correlated with COHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 774 (2.2%) missing valuesMissing
PM10 has 582 (1.7%) missing valuesMissing
SO2 has 628 (1.8%) missing valuesMissing
NO2 has 667 (1.9%) missing valuesMissing
CO has 1521 (4.3%) missing valuesMissing
O3 has 604 (1.7%) missing valuesMissing
RAIN is highly skewed (γ1 = 29.5690635)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33663 (96.0%) zerosZeros
WSPM has 417 (1.2%) zerosZeros

Reproduction

Analysis started2024-03-08 05:07:34.521396
Analysis finished2024-03-08 05:08:14.554565
Duration40.03 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:14.699262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:08:14.910482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:08:15.092774image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:08:15.264040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:15.429319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:08:15.604267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:15.827481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:08:16.021139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:16.240201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:08:16.431978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct481
Distinct (%)1.4%
Missing774
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean71.099743
Minimum2
Maximum882
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:16.649188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q118
median46
Q3100
95-th percentile222
Maximum882
Range880
Interquartile range (IQR)82

Descriptive statistics

Standard deviation72.326926
Coefficient of variation (CV)1.01726
Kurtosis4.7964482
Mean71.099743
Median Absolute Deviation (MAD)33
Skewness1.8753985
Sum2438010.2
Variance5231.1842
MonotonicityNot monotonic
2024-03-08T12:08:16.868037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 706
 
2.0%
12 699
 
2.0%
3 690
 
2.0%
10 687
 
2.0%
13 682
 
1.9%
14 679
 
1.9%
8 599
 
1.7%
9 597
 
1.7%
15 576
 
1.6%
16 535
 
1.5%
Other values (471) 27840
79.4%
(Missing) 774
 
2.2%
ValueCountFrequency (%)
2 3
 
< 0.1%
3 690
2.0%
4 216
 
0.6%
5 319
0.9%
6 435
1.2%
7 529
1.5%
8 599
1.7%
9 597
1.7%
10 687
2.0%
11 706
2.0%
ValueCountFrequency (%)
882 1
< 0.1%
662 1
< 0.1%
596 1
< 0.1%
581 1
< 0.1%
576 1
< 0.1%
560 1
< 0.1%
557 1
< 0.1%
552 1
< 0.1%
544 1
< 0.1%
540 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct562
Distinct (%)1.6%
Missing582
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean94.657871
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:17.050520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile12
Q134
median72
Q3131
95-th percentile256
Maximum999
Range997
Interquartile range (IQR)97

Descriptive statistics

Standard deviation83.441738
Coefficient of variation (CV)0.88150872
Kurtosis7.891491
Mean94.657871
Median Absolute Deviation (MAD)44
Skewness2.0484208
Sum3263992.7
Variance6962.5237
MonotonicityNot monotonic
2024-03-08T12:08:17.277442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 353
 
1.0%
22 349
 
1.0%
18 348
 
1.0%
24 346
 
1.0%
19 333
 
0.9%
20 328
 
0.9%
26 327
 
0.9%
13 326
 
0.9%
25 325
 
0.9%
15 325
 
0.9%
Other values (552) 31122
88.8%
(Missing) 582
 
1.7%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 44
 
0.1%
4 18
 
0.1%
5 181
0.5%
6 353
1.0%
7 171
0.5%
8 218
0.6%
9 212
0.6%
10 266
0.8%
11 248
0.7%
ValueCountFrequency (%)
999 1
< 0.1%
992 1
< 0.1%
980 1
< 0.1%
976 1
< 0.1%
933 1
< 0.1%
930 1
< 0.1%
858 2
< 0.1%
793 1
< 0.1%
775 1
< 0.1%
770 2
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct294
Distinct (%)0.9%
Missing628
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean14.958906
Minimum0.2856
Maximum310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:17.524612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q12
median7
Q318
95-th percentile58
Maximum310
Range309.7144
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20.975331
Coefficient of variation (CV)1.4021969
Kurtosis12.081466
Mean14.958906
Median Absolute Deviation (MAD)5
Skewness2.9385808
Sum515124.87
Variance439.96453
MonotonicityNot monotonic
2024-03-08T12:08:17.805166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9215
26.3%
3 2732
 
7.8%
4 1734
 
4.9%
5 1498
 
4.3%
6 1394
 
4.0%
7 1186
 
3.4%
8 1159
 
3.3%
9 995
 
2.8%
10 831
 
2.4%
11 753
 
2.1%
Other values (284) 12939
36.9%
ValueCountFrequency (%)
0.2856 11
 
< 0.1%
0.5712 7
 
< 0.1%
0.8568 4
 
< 0.1%
1 377
 
1.1%
1.1424 7
 
< 0.1%
1.428 7
 
< 0.1%
1.7136 10
 
< 0.1%
1.9992 11
 
< 0.1%
2 9215
26.3%
2.2 1
 
< 0.1%
ValueCountFrequency (%)
310 1
< 0.1%
257 1
< 0.1%
235 1
< 0.1%
215 1
< 0.1%
195 1
< 0.1%
192 2
< 0.1%
186 1
< 0.1%
184 1
< 0.1%
182 1
< 0.1%
180 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct459
Distinct (%)1.3%
Missing667
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean44.182086
Minimum1.8477
Maximum226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:17.980639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.8477
5-th percentile10
Q122
median36
Q360.3582
95-th percentile102
Maximum226
Range224.1523
Interquartile range (IQR)38.3582

Descriptive statistics

Standard deviation29.519796
Coefficient of variation (CV)0.66813949
Kurtosis1.5530672
Mean44.182086
Median Absolute Deviation (MAD)17
Skewness1.1932586
Sum1519731.2
Variance871.41837
MonotonicityNot monotonic
2024-03-08T12:08:18.194163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 684
 
2.0%
24 675
 
1.9%
25 673
 
1.9%
22 668
 
1.9%
17 661
 
1.9%
29 652
 
1.9%
21 650
 
1.9%
23 638
 
1.8%
18 630
 
1.8%
27 622
 
1.8%
Other values (449) 27844
79.4%
(Missing) 667
 
1.9%
ValueCountFrequency (%)
1.8477 1
 
< 0.1%
2 117
0.3%
2.2583 1
 
< 0.1%
3 55
 
0.2%
4 119
0.3%
4.7219 1
 
< 0.1%
5 115
0.3%
5.1325 1
 
< 0.1%
5.5431 1
 
< 0.1%
6 185
0.5%
ValueCountFrequency (%)
226 1
 
< 0.1%
208 1
 
< 0.1%
205 1
 
< 0.1%
203 1
 
< 0.1%
202 1
 
< 0.1%
201 1
 
< 0.1%
199 3
< 0.1%
198 1
 
< 0.1%
196 3
< 0.1%
195 3
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)0.3%
Missing1521
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean1152.3013
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:18.467278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1500
median800
Q31400
95-th percentile3300
Maximum10000
Range9900
Interquartile range (IQR)900

Descriptive statistics

Standard deviation1103.0563
Coefficient of variation (CV)0.95726373
Kurtosis10.427141
Mean1152.3013
Median Absolute Deviation (MAD)400
Skewness2.7280862
Sum38651644
Variance1216733.2
MonotonicityNot monotonic
2024-03-08T12:08:18.684177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 3193
 
9.1%
400 3170
 
9.0%
600 2800
 
8.0%
300 2763
 
7.9%
700 2477
 
7.1%
800 1978
 
5.6%
900 1595
 
4.5%
1000 1488
 
4.2%
1100 1358
 
3.9%
200 1333
 
3.8%
Other values (105) 11388
32.5%
(Missing) 1521
 
4.3%
ValueCountFrequency (%)
100 331
 
0.9%
200 1333
3.8%
300 2763
7.9%
400 3170
9.0%
500 3193
9.1%
600 2800
8.0%
700 2477
7.1%
800 1978
5.6%
900 1595
4.5%
1000 1488
4.2%
ValueCountFrequency (%)
10000 2
 
< 0.1%
9900 3
< 0.1%
9800 1
 
< 0.1%
9700 1
 
< 0.1%
9500 5
< 0.1%
9400 2
 
< 0.1%
9300 2
 
< 0.1%
9200 1
 
< 0.1%
9100 3
< 0.1%
9000 2
 
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct886
Distinct (%)2.6%
Missing604
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean57.940003
Minimum0.2142
Maximum429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:18.920275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q115.6366
median46
Q380
95-th percentile173
Maximum429
Range428.7858
Interquartile range (IQR)64.3634

Descriptive statistics

Standard deviation54.316674
Coefficient of variation (CV)0.93746413
Kurtosis3.0277304
Mean57.940003
Median Absolute Deviation (MAD)32
Skewness1.5832546
Sum1996612.5
Variance2950.3011
MonotonicityNot monotonic
2024-03-08T12:08:19.388569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 2273
 
6.5%
3 829
 
2.4%
4 717
 
2.0%
5 584
 
1.7%
6 576
 
1.6%
8 457
 
1.3%
7 450
 
1.3%
9 399
 
1.1%
10 357
 
1.0%
38 332
 
0.9%
Other values (876) 27486
78.4%
(Missing) 604
 
1.7%
ValueCountFrequency (%)
0.2142 4
 
< 0.1%
0.4284 3
 
< 0.1%
0.6426 6
 
< 0.1%
0.8568 3
 
< 0.1%
1 96
0.3%
1.071 9
 
< 0.1%
1.2852 7
 
< 0.1%
1.4994 9
 
< 0.1%
1.7136 7
 
< 0.1%
1.9278 11
 
< 0.1%
ValueCountFrequency (%)
429 1
< 0.1%
413 1
< 0.1%
387 1
< 0.1%
366 1
< 0.1%
365 1
< 0.1%
363 1
< 0.1%
360.4986 1
< 0.1%
359 1
< 0.1%
348 2
< 0.1%
346 1
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct998
Distinct (%)2.9%
Missing53
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean13.686111
Minimum-16.6
Maximum41.4
Zeros186
Zeros (%)0.5%
Negative5115
Negative (%)14.6%
Memory size274.1 KiB
2024-03-08T12:08:19.603403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.6
5-th percentile-4
Q13.4
median14.7
Q323.3
95-th percentile30.7
Maximum41.4
Range58
Interquartile range (IQR)19.9

Descriptive statistics

Standard deviation11.365313
Coefficient of variation (CV)0.83042675
Kurtosis-1.134788
Mean13.686111
Median Absolute Deviation (MAD)9.7
Skewness-0.098545321
Sum479164.44
Variance129.17034
MonotonicityNot monotonic
2024-03-08T12:08:19.819360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 240
 
0.7%
1 224
 
0.6%
-2 205
 
0.6%
2 202
 
0.6%
0 186
 
0.5%
-1 169
 
0.5%
4 149
 
0.4%
21.5 145
 
0.4%
22.9 142
 
0.4%
24.3 137
 
0.4%
Other values (988) 33212
94.7%
ValueCountFrequency (%)
-16.6 1
 
< 0.1%
-16.5 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16.1 1
 
< 0.1%
-15.9 1
 
< 0.1%
-15.8 2
< 0.1%
-15.6 1
 
< 0.1%
-15.5 2
< 0.1%
-15.4 2
< 0.1%
-15.3 3
< 0.1%
ValueCountFrequency (%)
41.4 1
< 0.1%
41 1
< 0.1%
40.5 2
< 0.1%
40 1
< 0.1%
39.8 2
< 0.1%
39.2 1
< 0.1%
38.9 1
< 0.1%
38.5 1
< 0.1%
38.4 1
< 0.1%
38.3 1
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct592
Distinct (%)1.7%
Missing50
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1007.7603
Minimum982.4
Maximum1036.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:20.055257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum982.4
5-th percentile992.4
Q1999.3
median1007.4
Q31016
95-th percentile1024.3
Maximum1036.5
Range54.1
Interquartile range (IQR)16.7

Descriptive statistics

Standard deviation10.225664
Coefficient of variation (CV)0.010146921
Kurtosis-0.91808004
Mean1007.7603
Median Absolute Deviation (MAD)8.4
Skewness0.10384267
Sum35285718
Variance104.56419
MonotonicityNot monotonic
2024-03-08T12:08:20.284251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1019 295
 
0.8%
1021 286
 
0.8%
1018 266
 
0.8%
1015 246
 
0.7%
1022 242
 
0.7%
1020 241
 
0.7%
1017 235
 
0.7%
1016 234
 
0.7%
1014 213
 
0.6%
1011 198
 
0.6%
Other values (582) 32558
92.9%
ValueCountFrequency (%)
982.4 1
< 0.1%
982.7 1
< 0.1%
982.8 1
< 0.1%
982.9 1
< 0.1%
983 1
< 0.1%
983.2 2
< 0.1%
983.3 1
< 0.1%
983.5 2
< 0.1%
983.7 2
< 0.1%
984 2
< 0.1%
ValueCountFrequency (%)
1036.5 1
 
< 0.1%
1036.3 1
 
< 0.1%
1036.2 1
 
< 0.1%
1036.1 1
 
< 0.1%
1036 3
< 0.1%
1035.9 3
< 0.1%
1035.8 1
 
< 0.1%
1035.7 1
 
< 0.1%
1035.6 1
 
< 0.1%
1035.5 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct602
Distinct (%)1.7%
Missing53
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.5054954
Minimum-35.1
Maximum27.2
Zeros66
Zeros (%)0.2%
Negative16287
Negative (%)46.4%
Memory size274.1 KiB
2024-03-08T12:08:20.464126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.1
5-th percentile-20.5
Q1-10.2
median1.8
Q314.2
95-th percentile21.4
Maximum27.2
Range62.3
Interquartile range (IQR)24.4

Descriptive statistics

Standard deviation13.822099
Coefficient of variation (CV)9.1810966
Kurtosis-1.176748
Mean1.5054954
Median Absolute Deviation (MAD)12.3
Skewness-0.14823158
Sum52708.9
Variance191.05042
MonotonicityNot monotonic
2024-03-08T12:08:20.691762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.6 137
 
0.4%
15 133
 
0.4%
15.7 126
 
0.4%
16.1 122
 
0.3%
15.3 120
 
0.3%
16.8 120
 
0.3%
16.7 119
 
0.3%
15.8 118
 
0.3%
17.5 116
 
0.3%
16.2 116
 
0.3%
Other values (592) 33784
96.3%
ValueCountFrequency (%)
-35.1 1
< 0.1%
-34.4 2
< 0.1%
-34.2 1
< 0.1%
-33.8 2
< 0.1%
-33.7 1
< 0.1%
-33.5 1
< 0.1%
-33.4 1
< 0.1%
-33 2
< 0.1%
-32.8 1
< 0.1%
-32.5 1
< 0.1%
ValueCountFrequency (%)
27.2 2
 
< 0.1%
27.1 2
 
< 0.1%
27 2
 
< 0.1%
26.9 5
< 0.1%
26.8 4
 
< 0.1%
26.7 2
 
< 0.1%
26.6 3
 
< 0.1%
26.5 1
 
< 0.1%
26.4 5
< 0.1%
26.3 10
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct116
Distinct (%)0.3%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.06036615
Minimum0
Maximum52.1
Zeros33663
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:20.954058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum52.1
Range52.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75289931
Coefficient of variation (CV)12.47221
Kurtosis1270.9726
Mean0.06036615
Median Absolute Deviation (MAD)0
Skewness29.569063
Sum2113.6
Variance0.56685737
MonotonicityNot monotonic
2024-03-08T12:08:21.155867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33663
96.0%
0.1 287
 
0.8%
0.2 146
 
0.4%
0.3 122
 
0.3%
0.5 79
 
0.2%
0.4 71
 
0.2%
0.7 52
 
0.1%
0.6 44
 
0.1%
0.9 42
 
0.1%
1 42
 
0.1%
Other values (106) 465
 
1.3%
(Missing) 51
 
0.1%
ValueCountFrequency (%)
0 33663
96.0%
0.1 287
 
0.8%
0.2 146
 
0.4%
0.3 122
 
0.3%
0.4 71
 
0.2%
0.5 79
 
0.2%
0.6 44
 
0.1%
0.7 52
 
0.1%
0.8 42
 
0.1%
0.9 42
 
0.1%
ValueCountFrequency (%)
52.1 1
< 0.1%
37.4 1
< 0.1%
28.9 1
< 0.1%
28.7 1
< 0.1%
26.5 1
< 0.1%
25.3 1
< 0.1%
23.7 1
< 0.1%
22.7 1
< 0.1%
22.3 1
< 0.1%
21.6 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing140
Missing (%)0.4%
Memory size274.1 KiB
NNW
4776 
NW
3838 
N
3777 
WNW
2877 
ESE
2786 
Other values (11)
16870 

Length

Max length3
Median length2
Mean length2.2299278
Min length1

Characters and Unicode

Total characters77878
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowENE
3rd rowENE
4th rowNNE
5th rowN

Common Values

ValueCountFrequency (%)
NNW 4776
13.6%
NW 3838
10.9%
N 3777
10.8%
WNW 2877
8.2%
ESE 2786
 
7.9%
E 2427
 
6.9%
NNE 1919
 
5.5%
SSE 1853
 
5.3%
SE 1823
 
5.2%
NE 1721
 
4.9%
Other values (6) 7127
20.3%

Length

2024-03-08T12:08:21.412053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nnw 4776
13.7%
nw 3838
11.0%
n 3777
10.8%
wnw 2877
8.2%
ese 2786
 
8.0%
e 2427
 
6.9%
nne 1919
 
5.5%
sse 1853
 
5.3%
se 1823
 
5.2%
ne 1721
 
4.9%
Other values (6) 7127
20.4%

Most occurring characters

ValueCountFrequency (%)
N 26908
34.6%
W 19194
24.6%
E 17925
23.0%
S 13851
17.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 77878
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 26908
34.6%
W 19194
24.6%
E 17925
23.0%
S 13851
17.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 77878
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 26908
34.6%
W 19194
24.6%
E 17925
23.0%
S 13851
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 26908
34.6%
W 19194
24.6%
E 17925
23.0%
S 13851
17.8%

WSPM
Real number (ℝ)

ZEROS 

Distinct95
Distinct (%)0.3%
Missing43
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.8538363
Minimum0
Maximum10
Zeros417
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:08:21.619925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11
median1.5
Q32.3
95-th percentile4.7
Maximum10
Range10
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.3098083
Coefficient of variation (CV)0.70653938
Kurtosis3.2071389
Mean1.8538363
Median Absolute Deviation (MAD)0.6
Skewness1.6592831
Sum64923.2
Variance1.7155979
MonotonicityNot monotonic
2024-03-08T12:08:21.859725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2 1934
 
5.5%
1.1 1928
 
5.5%
1 1922
 
5.5%
1.3 1859
 
5.3%
0.9 1787
 
5.1%
1.4 1589
 
4.5%
0.8 1569
 
4.5%
1.5 1524
 
4.3%
1.6 1443
 
4.1%
0.7 1351
 
3.9%
Other values (85) 18115
51.7%
ValueCountFrequency (%)
0 417
 
1.2%
0.1 201
 
0.6%
0.2 215
 
0.6%
0.3 157
 
0.4%
0.4 474
 
1.4%
0.5 722
2.1%
0.6 1043
3.0%
0.7 1351
3.9%
0.8 1569
4.5%
0.9 1787
5.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9.6 3
< 0.1%
9.4 1
 
< 0.1%
9.3 3
< 0.1%
9.2 3
< 0.1%
9.1 1
 
< 0.1%
9 1
 
< 0.1%
8.8 3
< 0.1%
8.6 2
 
< 0.1%
8.5 7
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Changping
35064 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters315576
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChangping
2nd rowChangping
3rd rowChangping
4th rowChangping
5th rowChangping

Common Values

ValueCountFrequency (%)
Changping 35064
100.0%

Length

2024-03-08T12:08:22.074686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:08:22.195511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
changping 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
n 70128
22.2%
g 70128
22.2%
C 35064
11.1%
h 35064
11.1%
a 35064
11.1%
p 35064
11.1%
i 35064
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 280512
88.9%
Uppercase Letter 35064
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 70128
25.0%
g 70128
25.0%
h 35064
12.5%
a 35064
12.5%
p 35064
12.5%
i 35064
12.5%
Uppercase Letter
ValueCountFrequency (%)
C 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 315576
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 70128
22.2%
g 70128
22.2%
C 35064
11.1%
h 35064
11.1%
a 35064
11.1%
p 35064
11.1%
i 35064
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 315576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 70128
22.2%
g 70128
22.2%
C 35064
11.1%
h 35064
11.1%
a 35064
11.1%
p 35064
11.1%
i 35064
11.1%

Interactions

2024-03-08T12:08:11.116770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:36.071921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:38.649223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:40.791493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:43.080167image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:45.489218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:47.571536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:49.875573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:52.220824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:54.569761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:56.753223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:58.906333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:01.080151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:04.161055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:08.692261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:11.288831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:36.252870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:38.826300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:40.951208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:43.223979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:45.670066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:47.719945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:50.023660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:52.359564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:54.736156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:56.897272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:59.046829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:01.251608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:04.451010image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:08.863024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:11.439691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:36.415253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:38.963848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:41.117302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:43.377333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:45.819272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:47.867065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:50.200706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:52.544338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:54.901944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:57.057298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:59.195647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:01.403596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:04.711887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:09.125438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:11.645927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:36.850310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:39.092894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:41.285985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:43.541398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:45.944077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:48.046875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:50.400656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:52.703385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:55.090698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:57.212440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:59.346148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:01.559024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:05.057469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:09.318019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:11.781448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:36.996505image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:39.230977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:41.440504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:43.657957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:46.061535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:48.207056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:50.549258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:52.819267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:55.211572image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:57.361061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:59.535780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:01.694753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:05.426635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:09.474560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:11.916993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:37.146861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:39.363074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:41.590007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:43.804690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:46.191895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:48.333571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:50.684015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:52.948351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:55.348682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:57.480635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:59.677169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:01.858396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:05.712864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:09.609852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:12.068210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:37.305913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:39.506226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:41.731876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:43.954680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:46.346931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:48.467995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:50.842940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:53.075246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:55.479864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:57.636517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:59.805804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:02.040222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:05.920221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:09.780885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:12.203722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:37.455709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:39.648699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:41.882758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:44.099633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:46.483307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:48.625499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:51.016670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:53.210158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:55.635246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:57.785893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:59.943866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:02.194162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:06.154014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:09.942898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:12.340654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:37.595506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:39.770834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:42.032769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:44.228381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:46.606612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:48.780771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:51.161001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:53.337587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:55.754934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:57.929631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:00.094334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:02.668589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:06.461756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:10.091754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:12.463166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:37.745854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:39.917203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:42.197292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:44.360586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:46.745497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:48.931612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:51.317832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:53.477554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:55.899179image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:58.066682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:00.229115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:02.837224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:06.855378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:10.230304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:12.595417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:37.895592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:40.066342image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:42.340465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:44.495721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:46.855112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:49.076200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:51.449103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:53.617746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:56.031226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:58.204410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:00.367467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:03.020014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:07.164487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:10.359847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:12.729697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:38.062200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:40.211854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:42.479209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:44.646269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:46.997674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:49.238560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:51.610469image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:54.015113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:56.167812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:58.353360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:00.505149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:03.226311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:07.526599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:10.514108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:12.860652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:38.213763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:40.348797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:42.645712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:44.795341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:47.148829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:49.403257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:51.771660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:54.153285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:56.305192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:58.492979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:00.634898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:03.486073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:07.918896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:10.665992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:12.990722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:38.360117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:40.500659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:42.786957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:45.191341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:47.283983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:49.569079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:51.931580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:54.293997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:56.452183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:58.641229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:00.788931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:03.727892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:08.276655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:10.836744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:13.138686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:38.514156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:40.640206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:42.923175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:45.347266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:47.426781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:49.731228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:52.079651image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:54.424788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:56.590822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:07:58.770292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:00.930158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:03.911201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:08.529732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:08:10.973772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:08:22.309438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.0330.7300.024-0.4370.7170.8230.1690.0150.522-0.269-0.373-0.019-0.0020.0430.0710.065
DEWP0.0331.000-0.137-0.0820.3290.1060.218-0.7700.182-0.4320.820-0.2370.0250.0000.2580.1080.150
NO20.730-0.1371.0000.047-0.6190.6200.6400.259-0.0520.489-0.359-0.3720.0150.0220.0250.0520.061
No0.024-0.0820.0471.000-0.146-0.076-0.0700.1640.019-0.261-0.1300.0810.0180.0010.0440.1000.862
O3-0.4370.329-0.619-0.1461.000-0.156-0.221-0.504-0.038-0.2400.6340.374-0.0170.241-0.1750.1390.070
PM100.7170.1060.620-0.076-0.1561.0000.884-0.055-0.0720.444-0.032-0.2110.0260.096-0.0460.0770.064
PM2.50.8230.2180.640-0.070-0.2210.8841.000-0.058-0.0040.434-0.035-0.3300.0120.038-0.0000.0740.062
PRES0.169-0.7700.2590.164-0.504-0.055-0.0581.000-0.0770.347-0.8410.0380.019-0.0400.0110.0730.144
RAIN0.0150.182-0.0520.019-0.038-0.072-0.004-0.0771.000-0.1550.032-0.051-0.0040.0070.0530.0140.006
SO20.522-0.4320.489-0.261-0.2400.4440.4340.347-0.1551.000-0.413-0.029-0.0000.045-0.2160.0370.113
TEMP-0.2690.820-0.359-0.1300.634-0.032-0.035-0.8410.032-0.4131.0000.0830.0150.1430.1260.1200.150
WSPM-0.373-0.237-0.3720.0810.374-0.211-0.3300.038-0.051-0.0290.0831.000-0.0150.139-0.1300.1800.077
day-0.0190.0250.0150.018-0.0170.0260.0120.019-0.004-0.0000.015-0.0151.0000.0000.0100.0260.000
hour-0.0020.0000.0220.0010.2410.0960.038-0.0400.0070.0450.1430.1390.0001.0000.0000.1670.000
month0.0430.2580.0250.044-0.175-0.046-0.0000.0110.053-0.2160.126-0.1300.0100.0001.0000.0850.249
wd0.0710.1080.0520.1000.1390.0770.0740.0730.0140.0370.1200.1800.0260.1670.0851.0000.103
year0.0650.1500.0610.8620.0700.0640.0620.1440.0060.1130.1500.0770.0000.0000.2490.1031.000

Missing values

2024-03-08T12:08:13.355046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:08:14.018829image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:08:14.343458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133103.06.013.07.0300.085.0-2.31020.8-19.70.0E0.5Changping
1220133113.03.06.06.0300.085.0-2.51021.3-19.00.0ENE0.7Changping
2320133123.03.022.013.0400.074.0-3.01021.3-19.90.0ENE0.2Changping
3420133133.06.012.08.0300.081.0-3.61021.8-19.10.0NNE1.0Changping
4520133143.03.014.08.0300.081.0-3.51022.3-19.40.0N2.1Changping
5620133153.03.010.017.0400.071.0-4.51022.6-19.50.0NNW1.7Changping
6720133164.06.012.022.0500.065.0-4.51023.4-19.50.0NNW1.8Changping
7820133173.06.025.039.0600.048.0-2.11024.6-20.00.0NW2.5Changping
8920133189.025.013.042.0700.046.0-0.21025.2-20.50.0NNW2.8Changping
910201331911.029.05.018.0500.073.00.61025.3-20.40.0NNW3.8Changping
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228148.015.05.018.0400.070.014.91008.4-12.40.0WNW4.2Changping
350553505620172281513.029.06.021.0400.070.015.61007.6-12.80.0WNW3.2Changping
350563505720172281615.037.06.022.0400.070.015.41007.2-12.90.0WNW4.4Changping
350573505820172281718.061.06.029.0400.051.014.71007.4-12.60.0WNW4.2Changping
350583505920172281818.057.0NaN2.0NaN2.013.41008.1-13.60.0WNW3.0Changping
350593506020172281928.047.04.014.0300.0NaN11.71008.9-13.30.0NNE1.3Changping
350603506120172282012.012.03.023.0500.064.010.91009.0-14.00.0N2.1Changping
35061350622017228217.023.05.017.0500.068.09.51009.4-13.00.0N1.5Changping
350623506320172282211.020.03.015.0500.072.07.81009.6-12.60.0NW1.4Changping
350633506420172282320.025.06.028.0900.054.07.01009.4-12.20.0N1.9Changping